2026/01/08 by Fernando Lima, Anna Agnew, Emily Schiedemeyer +1 · 1 voice
Engineering · #Architecture and Computational Design #Design Education and Practice #Urban Design and Spatial Analysis
paper · pdf · doi:10.3390/buildings16020265
openalex created_date 2026/01/08 · openalex publication_date 2026/01/08 · openalex updated_date 2026/07/30
Rapid urbanization in the Global South continues to intensify the growth of informal settlements, challenging architectural education to equip students with methods capable of addressing complex socio-spatial, environmental, and ethical conditions. While computational design and service-learning have each been explored within architectural pedagogy, their systematic integration, particularly in the context of informal settlement planning, remains underdeveloped. This article presents a pedagogical framework that combines pattern language theory, shape grammars, and parametric modeling with service-learning principles within an undergraduate architectural design studio. Implemented in ARC 4025 (Architecture Studio 5) at the O’More College of Architecture and Design, Belmont University, the framework guides students in translating empirical observations of an informal settlement in Ahmedabad, India, into rule-based and generative design systems. The methodology emphasizes process–product reciprocity, enabling students to encode settlement patterns as transformation rules that generate, test, and evaluate coherent urban forms across multiple scales. A detailed case study, Community at Scale, is presented as a proof of method, illustrating how analysis is converted into computational logic across dwelling, block, and neighborhood scales. Rather than proposing a finalized solution, the study demonstrates how computational design can operationalize morphogenetic reasoning within architectural education while remaining grounded in social responsibility. The article concludes by discussing pedagogical implications, situating the approach within urban morphology discourse, and outlining limitations and directions for future research.